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This paper aims to explore factors influencing apartment price volatility in the two biggest cities in Vietnam, Hanoi and Ho Chi Minh City.

The study uses the supply and demand approach and provides a literature review of previous studies to develop four main hypotheses using four determinants of apartment price volatility in Vietnam: gross domestic product (GDP), inflation rate, lending interest rate and construction cost. Subsequently, the Vector Error Correction Model (VECM) is used to analyze a monthly data sample of 117.

The research highlights the important role of construction costs in apartment price volatility in the two largest cities. Moreover, there are significant differences in how all four determinants affect apartment price volatility in the two cities. In addition, there is a long-run relationship between the determinants and apartment price volatility in both Hanoi and Ho Chi Minh City.

Limitations related to data transparency of the real estate industry in Vietnam lead to three main limitations of this paper, including: this paper only collects a sample of 117 valid monthly observations; apartment price volatility is calculated by changes in the apartment price index instead of apartment price standard deviation; and this paper is limited by only four determinants, those being GDP, inflation rate, lending interest rate and construction cost.

The study provides evidence of differences in how the above determinants affect apartment price volatility in Hanoi and Ho Chi Minh City, which helps investors and policymakers to make informed decisions relating to the real estate market in the two biggest cities in Vietnam.

This paper makes several recommendations to policymakers and investors in Vietnam to ensure a stable real estate market, contributing to the stability of the national economy.

This paper provides a new approach using VECM to analyze both long-run and short-run relationships between macroeconomic and sectoral independent variables and apartment price volatility in the two biggest cities in Vietnam.

Increasing overhang of serviced apartments poses a serious concern to the national property market. This study aims to examine the impacts of macroeconomic determinants, namely, gross domestic product (GDP), consumer confidence index (CF), existing stocks (ES), incoming supply (IS) and completed project (CP) on serviced apartment price changes.

To achieve more accurate, quality price changes, a serviced apartment price index (SAPI) was constructed through a self-developed hedonic price index model. This study has collected 1,567 transaction data in Kuala Lumpur, covering 2009Q1–2018Q4 for price index construction and data were analysed using the vector autoregressive model, the vector error correction model and the fully modified ordinary least squares (OLS) (FMOLS).

Results of the regression model show that only GDP, ES and IS were significantly associated with SAPI, with an R² of 0.7, where both ES and IS have inverse relationships with SAPI. More precisely, it is predicted that the price of serviced apartments will be reduced by 0.56% and 0.21% for every 1% increase in ES and IS, respectively.

Therefore, government monitoring of serviced apartments’ future supply is crucial by enforcing land use-planning regulations via stricter development approval of serviced apartments to safeguard and achieve more stable property prices.

By adopting an innovative approach to estimating the response of price change to supply and demand in a situation where there is no price indicator for serviced apartments, the study addresses the knowledge gap, especially in terms of understanding what are the key determinants of, and to what extent they influence, the SAPI.

Housing price is a barometer of a national economy. In recent years, Iran experienced high inflation in its economy, which affects everything, including housing. The purpose of this study is the estimation of the value of residential apartments of Tehran using ordinary least square (OLS) and geographically weighted regression (GWR) methods.

This paper proposed a method for determining the compound variables and used them to estimate and evaluate the prices in the district six of Tehran city. Also, this paper compared the GWR and OLS methods with different types of factors and their influences in house price estimations.

During the high inflation period of the study period, the age of buildings, inflation, parking, storage room and their locations are the most critical factors that affect the price of apartments in district six of Tehran. Besides, compound variables have the most influence on the prediction of the prices.

The exact location of the apartments in the study area were unknown. Therefore, the positions are extracted from their addresses. The uncertainty of location forced us to ignore the neighborhood terms in the hedonic method.

The exact locations of the apartments in the study area were unknown. Therefore, the positions are extracted from their addresses. The uncertainty of location forced us to ignore the neighborhood terms in the hedonic method.

The originality of the proposed method is that it used a different approach to determine the valid variables of the apartment prices. Also, the evaluation of the method showed that the proposed variables are significantly useful.

This study aims to examine the impact of relative importance of local characteristics, distance from the city centre and unobservable spatial relation in explaining values of constant‐quality apartment units in Vienna.

Drawing on recent developments in spatial econometrics and spatial hedonic house price modelling, the rent gradient hypothesis is examined by means of hedonic regression and spatial hedonic regression. Spatial autocorrelation tests are applied in order to assess possible presence of spatial dependence. The authors borrow Florax et al.'s specification search strategy in order to choose the most appropriate spatial model specification.

This research shows that local characteristics – or particularities – proxied by district and distance from the city centre are important location variables with regard to the Viennese apartment market. The spatial analysis suggests that the apartment prices are spatially autocorrelated and the Viennese apartment market has a distance‐based neighbourhood structure. The main finding is, however, that residents are willing to bid more for constant‐quality apartment units that are close to the centre of the city.

Rent gradient hypothesis is usually tested within non‐spatial hedonic frameworks: this study estimates a spatial hedonic model additionally in order to allow for comparison of results. This is also the first article to apply recent developments in spatial econometrics to examine explicitly rent gradient theory in the context of the Viennese apartment market.

This study examines the long term effects of macroeconomic fundamentals on apartment price dynamics in major metropolitan areas in Sweden and Germany.

The main approach is panel cointegration analysis that allows to overcome certain data restrictions such as spatial heterogeneity, cross-sectional dependence, and non-stationary, but cointegrated data. The Swedish dataset includes three cities over a period of 23 years, while the German dataset includes seven cities for 29 years. Analysis of apartment price dynamics include population, disposable income, mortgage interest rate, and apartment stock as underlying macroeconomic variables in the model.

The empirical results indicate that apartment prices react more strongly on changes in fundamental factors in major Swedish cities than in German ones despite quite similar development of these macroeconomic variables in the long run in both countries. On one hand, overreactions in apartment price dynamics might be considered as the evidence of the price bubble building in Sweden. On the other hand, these two countries differ in institutional arrangements of the housing markets, and these differences might contribute to the size of apartment price elasticities from changes in fundamentals. These arrangements include various banking sector policies, such as mortgage financing and valuation approaches, as well as different government regulations of the housing market as, for example, rent control.

In distinction to the previous studies carried out on Swedish and German data for single-family houses, this study focuses on the apartment segment of the market and examines apartment price elasticities from a long term perspective. In addition, the results from this study highlight the differences between the two countries at the city level in an integrated long run equilibrium framework.

Landscape view is a crucial factor in house-buying decisions. Landscape views provide an amenity to residents, and this can influence the house or apartment owners in their residence decisions. Yet, the relative value of different types of view potentially differs. Additionally, the value of each type of view may differ depending on an apartment’s elevation above the ground level. In this study, the authors aim to estimate the value of landscape views on apartment prices in major urban areas in Indonesia.

This paper evaluates to what extent various landscape views including mountain, sea, river, lake, street, urban village, garden and sports center views affect apartment prices in major urban areas in Indonesia. Two hedonic regression approaches are used: ordinary least squares and semiparametric regression. The latter is used to accommodate a possible non-linearity in the relationship between price and apartment characteristics. The model also incorporates housing and locational characteristics as control variables.

Using online apartment market data, the estimates in this paper show some degree of heterogeneity in the value of various views to the extent of providing negative externalities. Mountain, street and sports center views are associated with higher apartment prices. Sea, lake and garden views are statistically insignificant in explaining the prices. In contrast, the unappealing nature of the rivers and their surrounding creates a negative impact on prices. The estimates also suggest that an apartment’s floor height plays a significant role in the valuation of views.

There is little research on landscape view effects on apartment prices, especially in Indonesia. In addition, the relationship between the value of views and height preferences has seldom been analyzed. This paper provides the valuation of an extensive list of landscape views in urban areas in Indonesia. The estimation results also suggest that the value of views may differ depending on the floor on which an apartment lies.

There is a lack of understanding in the literature on the relation between parks and house price in relation to crime in Scandinavian context. This paper aims to investigate the effect of the amenity value of accessibility to parks on apartment prices with reference to crime rates in parks in Stockholm.

This paper analyses the effects of park proximity and crime in parks on apartment prices by using geographic information systems and hedonic modelling.

Findings show that the proximity of parks as an environmental amenity has an effect on apartment prices. The results also demonstrate that the impact of parks on apartment prices is different in the different segments of the apartment market in Stockholm. Moreover, various types of parks may differ in their impact, for instance, grass parks and park blocks are more desirable in Stockholm than landscape parks and neighbourhood parks. The effects of crimes in parks influence apartment prices negatively.

This paper provides a new methodology by using the shortest distance to a park as a main variable. The shortest distance to a park variable is considered a better choice than using park as an aggregate measure. To the best of the author’s knowledge, this is the first study that investigates the effect of specific park types, for instance, grass parks, neighbourhood parks, landscape parks and park blocks, in Stockholm housing market.

The purpose of this study is to identify which are the main factors influencing the apartment prices in Prishtina, the capital of Kosovo, during the period 2018–2020. The factors identified will be used by real estate developers and investors for better decision-making in apartment investments.

The research methodology is quantitative. The methodology analyzes 1,468 real estate transaction contracts of apartment buildings using a probability random sampling. The research methodology uses multiple regression analysis to identify whether the research model is significant in predicting apartment prices but also identify which are the main factors that influence the apartment prices in Prishtina, Kosovo.

The present study finds that location, size, floor, access to road and building quality affect apartment prices in Prishtina, whereas surprisingly access to green spaces and availability of parking spaces have no statistically significant effect on apartment prices in Kosovo.

The study has great implications for the real estate developers in Prishtina, Kosovo showing how to improve their decision-making process in real estate investments to know which characteristics are most valued by investors for investment in new apartment buildings in Prishtina, Kosovo and local authorities in Prishtina, Kosovo to modernize the access to road infrastructure for its inhabitants and adopt building regulations that will enforce strict criteria as far as building quality is concerned.

The study is the first quantitative study that studies the factors influencing the apartment prices in Prishtina, Kosovo.

The purpose of this paper is to determine the most significant factors influencing apartment values in Nairobi Metropolitan Area (NMA), Kenya and develop a valuation model using the identified variables. The paper is thus promoting the adoption of advanced, more reliable and accurate valuation methods in developing economies such as Kenya. Therefore, the paper demonstrates the potential of the hedonic pricing method (HPM) in property valuation by making objective adjustments to comparable sales data.

The paper utilizes a case study design by analysing recent (January 2020 to December 2021) apartment sales and apartment attributes in NMA. A sample size of 264 transactions was analysed using the HPM.

The study identified six significant factors affecting apartment values in the study area: apartment size, location, floor finishes, provision of shopping facilities, swimming pool and gym. A valuation model has been developed using the six variables.

The paper is contributing to the ongoing global debate on the accuracy, reliability and validity of property valuations with particular emphasis on the developing economies to adopt the more accurate, efficient and reliable valuation approaches.

The findings are useful to property valuers and estate managers in determining the most statistically significant attributes affecting apartment values in the NMA hence enabling them to offer informed professional advice. However, the findings of the study are limited to the study area since real estate is heterogeneous.

Property investors, financiers and other stakeholders in the society will benefit from the findings of this study in their decision-making process. Additionally, wide adoption of the more reliable and accurate valuation approaches would foster public confidence in valuation and estate management professional services leading to enhanced consumption of these services by the society and promoting public care.

The paper is promoting valuation accuracy, efficiency and reliability in the developing economies by advocating for advanced valuation methods. It is the first attempt to develop a valuation model for apartments in the NMA, the capital city of Kenya and the business hub of East and Central Africa.

This paper aims to scrutinize the value of houses explained by their location in Central Asia’s largest city – Tashkent, the capital of Uzbekistan. The city was completely rebuilt after a devastating earthquake of 1961 and since the country was under the centrally planned regime, houses followed a common standard and style. This allows minimizing the error from omitting possible intrinsic characteristics in a hedonic model, while measuring the impact of location on housing values.

This study uses unique data on housing values and characteristics harvested from classified adverts. This study deliberately focused on data between 2013 and 2016, when Uzbekistan was characterized as hesitant in economic reforms. Therefore, residential buildings constructed during the Former Soviet Union dominated all parts of the city in the analysis period. The results are based on the hedonic pricing model.

The results suggest that locational characteristics are relatively more important than intrinsic characteristics. This study finds that proximity to the city center and access to subway stations significantly increase housing values. The opposite is true for the case of proximity to a wastewater treatment plant. Surprisingly, on the contrary to the authors’ expectations access to a park has no economically significant impact on housing values. The results, therefore, call for an integrated approach for city planning.

The first and most important contribution of this study to the field of housing economics is that it is the first study done for Uzbekistan, the most populated Central Asian country, and it is a case study for other Central Asian countries. The second contribution of the study is that, after a long and meticulous study, the determinants of housing prices were analyzed by taking into account all possible intrinsic and extrinsic properties of houses with the similar characteristics, over a unique data set created from approximately 18,000 observations from classified pages of a major newspaper published every Thursday in Tashkent City. The final contribution of the study is that it offers new perspectives and policy recommendations to the housing market actors in Uzbekistan.